A Multifeature Autosegmentation-Based Approach for Inshore Ambiguity Identification and Suppression With Azimuth Multichannel SAR Systems

Abstract

To address the problems of location and suppression of inshore azimuth ambiguous clutter for the azimuth multichannel synthetic aperture radar (SAR) systems, a multifeature autosegmentation-based approach is developed in this paper. This proposed method can segment a SAR image automatically according to the distinctions among main land clutter, ambiguous land clutter, and sea clutter in the features of interferogram's phase and magnitude. First, the finite mixture clutter model for a multilook covariance matrix (MLCM) is built, where the off-diagonal elements of the MLCM contain the information of magnitude and interferogram's phase between azimuth channels. Then, SAR image autosegmentation is carried out by using the expectation maximum algorithm with combination of the aforementioned mixture model, and the isolated points that are segmented incorrectly can be eliminated via exploiting the Markov random field smoothing technique. Finally, azimuth ambiguous clutter can be suppressed by means of the clutter covariance matrix, which is constructed by the training samples of segmented ambiguities. The experiments on simulated data and real data measured by TerraSAR-X demonstrate that the proposed approach can obtain the more accurate position information and good cancellation performance for the azimuth ambiguous clutter, without the accurate system parameters and the information of the sources account for azimuth ambiguities.